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This is the first of a series of studies that explore the relationship between disciplinary background and the weighting of various elements of a manuscript in peer reviewers’ determination of publication recommendations. Research questions include: (1) To what extent are tacit criteria for determining quality or value of EER manuscripts influenced by reviewers’ varied disciplinary backgrounds and levels of expertise? and (2) To what extent does mentored peer review professional development influence reviewers’ EER manuscript evaluations? Data were collected from 27 mentors and mentees in a peer review professional development program. Participants reviewed the same two manuscripts, using a form to identify strengths, weaknesses, and recommendations. Responses were coded by two researchers (70% IRR). Our findings suggest that disciplinary background influences reviewers’ evaluation of EER manuscripts. We also found evidence that professional development can improve reviewers’ understanding of EER disciplinary conventions. Deeper understanding of the epistemological basis for manuscript reviews may reveal ways to strengthen professional preparation in engineering education as well as other disciplines.

This paper describes the Engineering Education Research (EER) Peer Review Training (PERT) project, which is designed to develop EER scholars’ peer review skills through mentored reviewing experiences. Supported by the National Science Foundation, the overall programmatic goals of the PERT project are to establish and evaluate a mentored reviewer program for 1) EER journal manuscripts and 2) EER grant proposals. Concurrently, the project seeks to explore how EER scholars develop schema for evaluating EER scholarship, whether these schema are shared in the community, and how schema influence recommendations made to journal editors during the peer review process. To accomplish these goals, the PERT project leveraged the previously established Journal of Engineering Education (JEE) Mentored Reviewer Program, where two researchers with little reviewing experience are paired with an experienced mentor to complete three manuscript reviews collaboratively. In this paper we report on focus group and exit survey findings from the JEE Mentored Reviewer Program and discuss revisions to the program in response to those findings.

This paper describes a peer reviewer mentoring program called the Engineering Education Research Peer Review Training (EER PERT) project and serves as a pilot study on longitudinal effects on researchers’ productivity and the impact of their work, differences in these factors for those who review journal manuscripts and those who review grant proposals, and what aspects of peer review training (knowledge, resources, collaborations, etc.) participants actually carry forward in their own research. Overall, the project seeks to investigate how engineering education research (EER) scholars develop skills and schema for reviewing scholarship, particularly in terms of developing constructive reviews that build expertise and advance knowledge. The Journal of Engineering Education (JEE) Mentored Reviewer Program constitutes the first phase of the EER PERT project. In this paper, we report on goals, structure and activities for the JEE Mentored Reviewer Program, pilot data from participants’ applications and exit surveys that will inform the EER PERT project in terms of participants’ developing skills and schema for reviewing and conducting EER, and provide initial suggestions from the training program that may benefit scholars new to EER.

Citizen scientists have the potential to expand scientific research. The virtual research assistant called VERA empowers citizen scientists to engage in environmental science in two ways. First, it automatically generates simulations based on the conceptual models of ecological phenomena for repeated testing and feedback. Second, it leverages the Encyclopedia of Life biodiversity knowledgebase to support the process of model construction and revision.

Abstract The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.

Abstract The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of simulated Monte Carlo events. Detector simulation with Geant4 is accurate but requires significant CPU resources. Over the past decade, ATLAS has developed and utilized tools that replace the most CPU-intensive component of the simulation—the calorimeter shower simulation—with faster simulation methods. Here, AtlFast3, the next generation of high-accuracy fast simulation in ATLAS, is introduced. AtlFast3 combines parameterized approaches with machine-learning techniques and is deployed to meet current and future computing challenges, and simulation needs of the ATLAS experiment. With highly accurate performance and significantly improved modelling of substructure within jets, AtlFast3 can simulate large numbers of events for a wide range of physics processes.

A bstract Measurements of the production cross-sections of the Standard Model (SM) Higgs boson ( H ) decaying into a pair of τ -leptons are presented. The measurements use data collected with the ATLAS detector from pp collisions produced at the Large Hadron Collider at a centre-of-mass energy of $$ \sqrt{s} $$ s = 13 TeV, corresponding to an integrated luminosity of 139 fb − 1 . Leptonic ( τ → ℓν ℓ ν τ ) and hadronic ( τ → hadrons ν τ ) decays of the τ -lepton are considered. All measurements account for the branching ratio of H → ττ and are performed with a requirement |y H | < 2 . 5, where y H is the true Higgs boson rapidity. The cross-section of the pp → H → ττ process is measured to be 2 . 94 ± $$ 0.21{\left(\mathrm{stat}\right)}_{-0.32}^{+0.37} $$ 0.21 stat − 0.32 + 0.37 (syst) pb, in agreement with the SM prediction of 3 . 17 ± 0 . 09 pb. Inclusive cross-sections are determined separately for the four dominant production modes: 2 . 65 ± $$ 0.41{\left(\mathrm{stat}\right)}_{-0.67}^{+0.91} $$ 0.41 stat − 0.67 + 0.91 (syst) pb for gluon-gluon fusion, 0 .more »197 ± $$ 0.028{\left(\mathrm{stat}\right)}_{-0.026}^{+0.032} $$ 0.028 stat − 0.026 + 0.032 (syst) pb for vector-boson fusion, 0 . 115 ± $$ 0.058{\left(\mathrm{stat}\right)}_{-0.040}^{+0.042} $$ 0.058 stat − 0.040 + 0.042 (syst) pb for vector-boson associated production, and 0 . 033 ± $$ 0.031{\left(\mathrm{stat}\right)}_{-0.017}^{+0.022} $$ 0.031 stat − 0.017 + 0.022 (syst) pb for top-quark pair associated production. Measurements in exclusive regions of the phase space, using the simplified template cross-section framework, are also performed. All results are in agreement with the SM predictions.« less

Abstract The energy response of the ATLAS calorimeter is measured for single charged pions with transverse momentum in the range $$10more »response in the hadronic calorimeter are also compared between data and simulation.« less